The Matching Law

Poling, Alan; Edwards, Timothy L.; Weeden, Marc; Foster, Timothy

doi:10.1007/bf03395762

Cited by 35 publications

(28 citation statements)

References 28 publications

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“…This is truly a law, and is a tautology, as all laws are (Rachlin, 1971). Equation (3), perhaps, might be called the "generalized matching law," but that term nowadays denotes a more specific version, even though that equation is not really a law in the usual sense, but an empirical generalization (Baum, 1974(Baum, , 1979Poling, Edwards, Weeden, & Foster, 2011). Though I originated the equation, I did not name it as such, but the usage has become common (Poling et al, 2011).…”

Section: The Law Of Allocationmentioning

confidence: 99%

“…Equation (3), perhaps, might be called the "generalized matching law," but that term nowadays denotes a more specific version, even though that equation is not really a law in the usual sense, but an empirical generalization (Baum, 1974(Baum, , 1979Poling, Edwards, Weeden, & Foster, 2011). Though I originated the equation, I did not name it as such, but the usage has become common (Poling et al, 2011). Equation (3) may be rewritten in a variety of ways (Baum, 2012b), but it is general enough for present purposes to be called the Law of Allocation.…”

Section: The Law Of Allocationmentioning

confidence: 99%

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Multiscale behavior analysis and molar behaviorism: An overview

Baum

2018

J Exper Analysis Behavior

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In the context of evolutionary theory, behavior is the interaction between the organism and its environment. Two implications follow: (a) behavior takes time; and (b) behavior is defined by its function. That behavior takes time implies that behavioral units are temporally extended patterns or activities. An activity functions as an integrated whole composed of parts that are themselves smaller‐scale activities. That behavior is defined by its function implies that behavior functions to change the environment in ways that promote reproductive success. Phylogenetically important events (PIEs) are enhanced or mitigated by activities they induce as a result of natural selection. Induction explains all the phenomena that have traditionally been explained by reinforcement. This multiscale view replaces discrete responses and contiguity with multiscale activities and covariance. A PIE induces operant activity as a result of covariance in the form of a feedback relation between the activity and the PIE. A signal (conditional inducer) induces PIE‐induced activities as a result of covariance between the PIE and the signal. In an ontological perspective, behavior is a process, and an activity is a process individual. For example, ontological considerations clarify the status of delay and probability discounting. A true natural science of behavior is possible.

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Section: The Law Of Allocationmentioning

confidence: 99%

Section: The Law Of Allocationmentioning

confidence: 99%

Multiscale behavior analysis and molar behaviorism: An overview

Baum

2018

J Exper Analysis Behavior

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“…Choice is central to foraging: how animals allocate their choices under various environmental conditions to optimize their procurement of food and other necessities such as water and mates [1][2][3][4]. According to this Darwinian perspective, a suite of adaptations allows animals to identify the distribution of responses across options that maximize the returns on their choices.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of pre- and post-choice behaviour: rats approximate optimal strategy in a discrete-trial decision task

2015

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We simulate two types of environments to investigate how closely rats approximate optimal foraging. Rats initiated a trial where they chose between two spouts for sucrose, which was delivered at distinct probabilities. The discrete trial procedure used allowed us to observe the relationship between choice proportions, response latencies and obtained rewards. Our results show that rats approximate the optimal strategy across a range of environments that differ in the average probability of reward as well as the dynamics of the depletion-renewal cycle. We found that the constituent components of a single choice differentially reflect environmental contingencies. Post-choice behaviour, measured as the duration of time rats spent licking at the spouts on unrewarded trials, was the most sensitive index of environmental variables, adjusting most rapidly to changes in the environment. These findings have implications for the role of confidence in choice outcomes for guiding future choices.

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“…Beyond a participant avoiding social norms that conflict with their tribe, a general reticence of a zero-sum participant to consider positive-behaviour may also be explained by matching law (Poling, Edwards, Weeden, & Foster, 2011). This law has empirically found that animals and people create heuristics, the strength of which is mathematically predicted upon the relative size and likelihood of reward that had been received from past use of that strategy (Snyder, Stoolmiller, Patterson, & Schrepferman, 2003).…”

Section: Perceptions Of Identity Threatmentioning

confidence: 99%

Do Self-Interested Learners Avoid Prosocial Learning?

Heslop¹,

Stojanovski²,

Paul³

et al. 2018

Preprint

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This article assumes that collaborators who adopt a positive-sum, prosocial perspective can build trust-based relationships, which is equivalent to collaborative ability (CA). To improve a collaborator’s CA, we anticipated that content based upon a model of positive-sum collaboration, founded upon ancestral behaviours presented as social norms, might alter a zero-sum, proself, collaborator’s perspective. However, we also postulated that learner’s willingness to engage in positive-sum content may rely on pre-existing prosociality. Intervention participant’s prosociality was assessed via self-assessment of their emotional intelligence (EQ), quality of life (QoL), and goal orientation (GO). Learner’s engagement in online content was assessed by the three stage reached: examine website front matter; examine website content; learn website content. Tukey’s multiple comparison showed a statistically significant difference between stages one and three, in the mean of respondent’s self-assessed QoL. This suggests that participants who wished to learn content had, on average, higher quality of life, and potentially therefore greater prosociality. In general, these findings support a claim that any intervention designed to increase individual prosociality may need to consider the target population’s reticence to engage.

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The Matching Law

Cited by 35 publications

References 28 publications

Multiscale behavior analysis and molar behaviorism: An overview

Multiscale behavior analysis and molar behaviorism: An overview

Dynamics of pre- and post-choice behaviour: rats approximate optimal strategy in a discrete-trial decision task

Do Self-Interested Learners Avoid Prosocial Learning?

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